Machine learning techniques and data for stock market forecasting: A literature review

Kumbure, Mahinda Mailagaha; Lohrmann, Christoph; Luukka, Pasi; Porras, Jari

doi:10.1016/j.eswa.2022.116659

Cited by 212 publications

(101 citation statements)

References 188 publications

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“…In their reference paper, de Oliveira, Nobre, and Zarate (2013) also quite clearly summarize the importance of directional predictions and emphasize that what really matters for decision making is predicting directions of movements. However, RMSE is one of the most popular metric in evaluation stock market prediction models; accuracy is also widely preferred likewise RMSE and most of the studies in the recent literature are based on classi cation-based prediction tasks (Kumbure et al, 2022). Moreover, for the stock market direction prediction or classi cation task, accuracy is the only metric to be employed.…”

Section: Methodsmentioning

confidence: 99%

Weak-Form Market Efficiency and Corruption: A Cross-Country Comparative Analysis

İcan

Çeli̇k

2022

Preprint

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Financial time series are hard to predict and generally characterized by nonlinear features. Market predictability is mostly a consequence of its efficiency. It has been discussed extensively under Efficient Market Hypothesis and related discussions. With the recent developments in computational methods, it is shown that many approaches such as machine learning and evolutionary computational techniques are successful in predicting financial time series. On the other hand, there is a general consensus among market participants and scholars that developed markets are more efficient than emerging ones indicating that, predicting developed markets are relatively harder using past data. It is thought that the economic and administrative conditions of countries normatively have an effect on the economy and level of market development. Moreover, it is of great importance for a healthy economy whether the public institutions and organizations are transparent and functioning in accordance with their purpose. The aim of this study is to show whether there is a relationship between transparency and market efficiency. In order to accomplish this task, for each of the seventy three countries, it is examined whether there is a relationship between the predictability of the national stock market indices and the corruption levels. To achieve this goal, correlation analysis has been conducted between prediction accuracy rates which are obtained by seven different machine learning algorithms and Corruption Perception Index levels. Classification codes: G14, G17, G18, C45, C53, C58

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Section: Methodsmentioning

confidence: 99%

Weak-Form Market Efficiency and Corruption: A Cross-Country Comparative Analysis

İcan

Çeli̇k

2022

Preprint

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“…ML is considered the working horse in the new era of the so-called big data. Different machine learning techniques have been applied successfully in diverse fields, such as, from wireless communications ( Tan et al, 2014 ), computer vision ( Khan et al, 2021 , Altantawy et al, 2020 ), finance ( Kumbure et al, 2022 ), entertainment ( Porcino et al, 2022 ), control system ( Hedrea and Petriu, 2021 ) and computational biology to biomedical and medical applications ( Chiang et al, 2014 , Albu et al, 2019 , Upadhyay and Nagpal, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model

Altantawy

Kishk²

2023

Expert Systems with Applications

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“…However, the behavior of a stock market is affected by many factors, such as financial and economic policy, business development, and investor psychology [1]. Due to complex internal factors and the changing economic environment, forecasting the stock market is a challenge for researchers of financial data mining [2]. Traditional stock market forecasting methods include securities investment analysis, nonlinear dynamic methods, time series mining, and statistical modeling [3].…”

Section: Introductionmentioning

confidence: 99%

“…Comparison results between EMTL-LS-SVR(L + R) and other algorithms on average rank deviations.Remark: In Table4, set d to represent the absolute value of the difference between average rank deviation and the CD value. * denotes d is between [0, 1], ** denotes d is between[1,2], *** denotes d is between[2,3], **** denotes d is between[3,4].…”

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confidence: 99%

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

Zhang

et al. 2022

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Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training all tasks simultaneously. With this motivation, we assume that the related tasks are close enough to share a common model whereas having their own independent models. Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SVR) algorithm, and an extension, EMTL-LS-SVR. Theoretical analysis shows that these models can be converted to linear systems. A Krylov-Cholesky algorithm is introduced to determine the optimal solutions of the models. We tested the proposed models by applying them to forecasts of the Chinese stock market index trend and the stock prices of five stated-owned banks. The experimental results demonstrate their validity.

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Machine learning techniques and data for stock market forecasting: A literature review

Cited by 212 publications

References 188 publications

Weak-Form Market Efficiency and Corruption: A Cross-Country Comparative Analysis

Weak-Form Market Efficiency and Corruption: A Cross-Country Comparative Analysis

Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

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